Differences in ride-hailing adoption by older Californians among types of locations
Bibliographic record
Abstract
Ride-hailing services such as Lyft and Uber can complement rides offered by family, friends, paid providers, and public transit. To learn why older adults might wish to use ride-hail, we conducted an online survey of 2,917 California respondents age 55 and older. Participants were asked whether they would value four features hypothesized to be benefits of ride-hailing. We specified binary logit models and used market segmentation to investigate whether there were location-based differences in the use of ride-hailing. Our analysis showed that women, city dwellers, persons with disabilities, and those who rely on others for rides were more open to ride-hailing. Women in suburbs or small town/rural settings were more likely to ride-hail than their male counterparts for reasons of independence, fear of being lost while driving, or getting help with carrying bags. Urban women, in contrast, were less likely than their male counterparts to ride-hail for these reasons. High-income individuals in suburbs or small town/rural locations were more likely to ride-hail than low-income respondents, while high-income urban residents were less likely to ride-hail. Adoption of ride-hailing services and the reasons for doing so showed strong variability by location even among respondents with similar socio-demographic attributes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".